INSERM, U1099, Rennes, F-35000, France; Université de Rennes 1, LTSI, Rennes, F-35000, France; CHU Rennes, CIC Inserm 1414, Rennes, F-35000, France; CHU Rennes, Centre de Données Cliniques, Rennes, F-35000, France.
INSERM, U1099, Rennes, F-35000, France; Université de Rennes 1, LTSI, Rennes, F-35000, France; Université de Rennes 2, IRMAR, Rennes, F-35000, France.
Comput Methods Programs Biomed. 2018 Feb;154:153-160. doi: 10.1016/j.cmpb.2017.11.012. Epub 2017 Nov 15.
Influenza epidemics are a major public health concern and require a costly and time-consuming surveillance system at different geographical scales. The main challenge is being able to predict epidemics. Besides traditional surveillance systems, such as the French Sentinel network, several studies proposed prediction models based on internet-user activity. Here, we assessed the potential of hospital big data to monitor influenza epidemics.
We used the clinical data warehouse of the Academic Hospital of Rennes (France) and then built different queries to retrieve relevant information from electronic health records to gather weekly influenza-like illness activity.
We found that the query most highly correlated with Sentinel network estimates was based on emergency reports concerning discharged patients with a final diagnosis of influenza (Pearson's correlation coefficient (PCC) of 0.931). The other tested queries were based on structured data (ICD-10 codes of influenza in Diagnosis-related Groups, and influenza PCR tests) and performed best (PCC of 0.981 and 0.953, respectively) during the flu season 2014-15. This suggests that both ICD-10 codes and PCR results are associated with severe epidemics. Finally, our approach allowed us to obtain additional patients' characteristics, such as the sex ratio or age groups, comparable with those from the Sentinel network.
Conclusions: Hospital big data seem to have a great potential for monitoring influenza epidemics in near real-time. Such a method could constitute a complementary tool to standard surveillance systems by providing additional characteristics on the concerned population or by providing information earlier. This system could also be easily extended to other diseases with possible activity changes. Additional work is needed to assess the real efficacy of predictive models based on hospital big data to predict flu epidemics.
流感疫情是一个重大的公共卫生关注点，需要在不同的地理尺度上建立一个成本高昂且耗时的监测系统。主要挑战在于能够进行预测。除了法国 Sentinel 网络等传统监测系统外，还有几项研究提出了基于互联网用户活动的预测模型。在这里，我们评估了医院大数据监测流感疫情的潜力。
我们使用了雷恩学术医院的临床数据仓库，然后构建了不同的查询来从电子健康记录中检索相关信息，以收集每周流感样疾病活动情况。
我们发现，与 Sentinel 网络估计最相关的查询是基于有关因流感而出院的患者的急诊报告（流感相关诊断组中的流感 ICD-10 代码和流感 PCR 检测），表现最佳（分别为 0.981 和 0.953）。这表明，无论是 ICD-10 代码还是 PCR 结果都与严重的疫情有关。最后，我们的方法使我们能够获得更多的患者特征，例如性别比或年龄组，与 Sentinel 网络的数据相当。
医院大数据似乎具有实时监测流感疫情的巨大潜力。这种方法可以通过提供有关受关注人群的其他特征或更早地提供信息，成为标准监测系统的补充工具。该系统还可以轻松扩展到其他可能发生活动变化的疾病。需要进一步的工作来评估基于医院大数据的预测模型预测流感疫情的实际效果。
